{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<font color = \"mediumblue\">Note: Notebook was updated July 2, 2019 with bug fixes.</font>\n",
    "\n",
    "#### If you were working on the older version:\n",
    "* Please click on the \"Coursera\" icon in the top right to open up the folder directory.  \n",
    "* Navigate to the folder: Week 3/ Planar data classification with one hidden layer.  You can see your prior work in version 5: Planar data classification with one hidden layer v5.ipynb\n",
    "\n",
    "#### List of bug fixes and enhancements\n",
    "* Clarifies that the classifier will learn to classify regions as either red or blue.\n",
    "* compute_cost function fixes np.squeeze by casting it as a float.\n",
    "* compute_cost instructions clarify the purpose of np.squeeze.\n",
    "* compute_cost clarifies that \"parameters\" parameter is not needed, but is kept in the function definition until the auto-grader is also updated.\n",
    "* nn_model removes extraction of parameter values, as the entire parameter dictionary is passed to the invoked functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Planar data classification with one hidden layer\n",
    "\n",
    "Welcome to your week 3 programming assignment. It's time to build your first neural network, which will have a hidden layer. You will see a big difference between this model and the one you implemented using logistic regression. \n",
    "\n",
    "**You will learn how to:**\n",
    "- Implement a 2-class classification neural network with a single hidden layer\n",
    "- Use units with a non-linear activation function, such as tanh \n",
    "- Compute the cross entropy loss \n",
    "- Implement forward and backward propagation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Packages ##\n",
    "\n",
    "Let's first import all the packages that you will need during this assignment.\n",
    "- [numpy](https://www.numpy.org/) is the fundamental package for scientific computing with Python.\n",
    "- [sklearn](http://scikit-learn.org/stable/) provides simple and efficient tools for data mining and data analysis. \n",
    "- [matplotlib](http://matplotlib.org) is a library for plotting graphs in Python.\n",
    "- testCases provides some test examples to assess the correctness of your functions\n",
    "- planar_utils provide various useful functions used in this assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Package imports\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from testCases_v2 import *\n",
    "import sklearn\n",
    "import sklearn.datasets\n",
    "import sklearn.linear_model\n",
    "from planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "np.random.seed(1) # set a seed so that the results are consistent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Dataset ##\n",
    "\n",
    "First, let's get the dataset you will work on. The following code will load a \"flower\" 2-class dataset into variables `X` and `Y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, Y = load_planar_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the dataset using matplotlib. The data looks like a \"flower\" with some red (label y=0) and some blue (y=1) points. Your goal is to build a model to fit this data. In other words, we want the classifier to define regions as either red or blue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the data:\n",
    "plt.scatter(X[0, :], X[1, :], c=Y.reshape(X[0,:].shape), s=40, cmap=plt.cm.Spectral);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have:\n",
    "    - a numpy-array (matrix) X that contains your features (x1, x2)\n",
    "    - a numpy-array (vector) Y that contains your labels (red:0, blue:1).\n",
    "\n",
    "Lets first get a better sense of what our data is like. \n",
    "\n",
    "**Exercise**: How many training examples do you have? In addition, what is the `shape` of the variables `X` and `Y`? \n",
    "\n",
    "**Hint**: How do you get the shape of a numpy array? [(help)](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.shape.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of X is: (2, 400)\n",
      "The shape of Y is: (1, 400)\n",
      "I have m = 400 training examples!\n"
     ]
    }
   ],
   "source": [
    "### START CODE HERE ### (≈ 3 lines of code)\n",
    "shape_X = X.shape\n",
    "shape_Y = Y.shape\n",
    "m = X.shape[1]  # training set size\n",
    "### END CODE HERE ###\n",
    "\n",
    "print ('The shape of X is: ' + str(shape_X))\n",
    "print ('The shape of Y is: ' + str(shape_Y))\n",
    "print ('I have m = %d training examples!' % (m))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "       \n",
    "<table style=\"width:20%\">\n",
    "  \n",
    "  <tr>\n",
    "    <td>**shape of X**</td>\n",
    "    <td> (2, 400) </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**shape of Y**</td>\n",
    "    <td>(1, 400) </td> \n",
    "  </tr>\n",
    "  \n",
    "    <tr>\n",
    "    <td>**m**</td>\n",
    "    <td> 400 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Simple Logistic Regression\n",
    "\n",
    "Before building a full neural network, lets first see how logistic regression performs on this problem. You can use sklearn's built-in functions to do that. Run the code below to train a logistic regression classifier on the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "D:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# Train the logistic regression classifier\n",
    "clf = sklearn.linear_model.LogisticRegressionCV();\n",
    "clf.fit(X.T, Y.T);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can now plot the decision boundary of these models. Run the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression: 47 % (percentage of correctly labelled datapoints)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the decision boundary for logistic regression\n",
    "plot_decision_boundary(lambda x: clf.predict(x), X, Y)\n",
    "plt.title(\"Logistic Regression\")\n",
    "\n",
    "# Print accuracy\n",
    "LR_predictions = clf.predict(X.T)\n",
    "print ('Accuracy of logistic regression: %d ' % float((np.dot(Y,LR_predictions) + np.dot(1-Y,1-LR_predictions))/float(Y.size)*100) +\n",
    "       '% ' + \"(percentage of correctly labelled datapoints)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:20%\">\n",
    "  <tr>\n",
    "    <td>**Accuracy**</td>\n",
    "    <td> 47% </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation**: The dataset is not linearly separable, so logistic regression doesn't perform well. Hopefully a neural network will do better. Let's try this now! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Neural Network model\n",
    "\n",
    "Logistic regression did not work well on the \"flower dataset\". You are going to train a Neural Network with a single hidden layer.\n",
    "\n",
    "**Here is our model**:\n",
    "<img src=\"images/classification_kiank.png\" style=\"width:600px;height:300px;\">\n",
    "\n",
    "**Mathematically**:\n",
    "\n",
    "For one example $x^{(i)}$:\n",
    "$$z^{[1] (i)} =  W^{[1]} x^{(i)} + b^{[1]}\\tag{1}$$ \n",
    "$$a^{[1] (i)} = \\tanh(z^{[1] (i)})\\tag{2}$$\n",
    "$$z^{[2] (i)} = W^{[2]} a^{[1] (i)} + b^{[2]}\\tag{3}$$\n",
    "$$\\hat{y}^{(i)} = a^{[2] (i)} = \\sigma(z^{ [2] (i)})\\tag{4}$$\n",
    "$$y^{(i)}_{prediction} = \\begin{cases} 1 & \\mbox{if } a^{[2](i)} > 0.5 \\\\ 0 & \\mbox{otherwise } \\end{cases}\\tag{5}$$\n",
    "\n",
    "Given the predictions on all the examples, you can also compute the cost $J$ as follows: \n",
    "$$J = - \\frac{1}{m} \\sum\\limits_{i = 0}^{m} \\large\\left(\\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right)  \\large  \\right) \\small \\tag{6}$$\n",
    "\n",
    "**Reminder**: The general methodology to build a Neural Network is to:\n",
    "    1. Define the neural network structure ( # of input units,  # of hidden units, etc). \n",
    "    2. Initialize the model's parameters\n",
    "    3. Loop:\n",
    "        - Implement forward propagation\n",
    "        - Compute loss\n",
    "        - Implement backward propagation to get the gradients\n",
    "        - Update parameters (gradient descent)\n",
    "\n",
    "You often build helper functions to compute steps 1-3 and then merge them into one function we call `nn_model()`. Once you've built `nn_model()` and learnt the right parameters, you can make predictions on new data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 - Defining the neural network structure ####\n",
    "\n",
    "**Exercise**: Define three variables:\n",
    "    - n_x: the size of the input layer\n",
    "    - n_h: the size of the hidden layer (set this to 4) \n",
    "    - n_y: the size of the output layer\n",
    "\n",
    "**Hint**: Use shapes of X and Y to find n_x and n_y. Also, hard code the hidden layer size to be 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: layer_sizes\n",
    "\n",
    "def layer_sizes(X, Y):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    X -- input dataset of shape (input size, number of examples)\n",
    "    Y -- labels of shape (output size, number of examples)\n",
    "    \n",
    "    Returns:\n",
    "    n_x -- the size of the input layer\n",
    "    n_h -- the size of the hidden layer\n",
    "    n_y -- the size of the output layer\n",
    "    \"\"\"\n",
    "    ### START CODE HERE ### (≈ 3 lines of code)\n",
    "    n_x = X.shape[0] # size of input layer\n",
    "    n_h = 4\n",
    "    n_y = Y.shape[0] # size of output layer\n",
    "    ### END CODE HERE ###\n",
    "    return (n_x, n_h, n_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of the input layer is: n_x = 5\n",
      "The size of the hidden layer is: n_h = 4\n",
      "The size of the output layer is: n_y = 2\n"
     ]
    }
   ],
   "source": [
    "X_assess, Y_assess = layer_sizes_test_case()\n",
    "(n_x, n_h, n_y) = layer_sizes(X_assess, Y_assess)\n",
    "print(\"The size of the input layer is: n_x = \" + str(n_x))\n",
    "print(\"The size of the hidden layer is: n_h = \" + str(n_h))\n",
    "print(\"The size of the output layer is: n_y = \" + str(n_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output** (these are not the sizes you will use for your network, they are just used to assess the function you've just coded).\n",
    "\n",
    "<table style=\"width:20%\">\n",
    "  <tr>\n",
    "    <td>**n_x**</td>\n",
    "    <td> 5 </td> \n",
    "  </tr>\n",
    "  \n",
    "    <tr>\n",
    "    <td>**n_h**</td>\n",
    "    <td> 4 </td> \n",
    "  </tr>\n",
    "  \n",
    "    <tr>\n",
    "    <td>**n_y**</td>\n",
    "    <td> 2 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 - Initialize the model's parameters ####\n",
    "\n",
    "**Exercise**: Implement the function `initialize_parameters()`.\n",
    "\n",
    "**Instructions**:\n",
    "- Make sure your parameters' sizes are right. Refer to the neural network figure above if needed.\n",
    "- You will initialize the weights matrices with random values. \n",
    "    - Use: `np.random.randn(a,b) * 0.01` to randomly initialize a matrix of shape (a,b).\n",
    "- You will initialize the bias vectors as zeros. \n",
    "    - Use: `np.zeros((a,b))` to initialize a matrix of shape (a,b) with zeros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_parameters\n",
    "\n",
    "def initialize_parameters(n_x, n_h, n_y):\n",
    "    \"\"\"\n",
    "    Argument:\n",
    "    n_x -- size of the input layer\n",
    "    n_h -- size of the hidden layer\n",
    "    n_y -- size of the output layer\n",
    "    \n",
    "    Returns:\n",
    "    params -- python dictionary containing your parameters:\n",
    "                    W1 -- weight matrix of shape (n_h, n_x)\n",
    "                    b1 -- bias vector of shape (n_h, 1)\n",
    "                    W2 -- weight matrix of shape (n_y, n_h)\n",
    "                    b2 -- bias vector of shape (n_y, 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(2) # we set up a seed so that your output matches ours although the initialization is random.\n",
    "\n",
    "    \n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = np.random.randn(n_h, n_x) * 0.01\n",
    "    b1 = np.zeros((n_h, 1))\n",
    "    W2 = np.random.randn(n_y, n_h) * 0.01\n",
    "    b2 = np.zeros((n_y, 1))\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    assert (W1.shape == (n_h, n_x))\n",
    "    assert (b1.shape == (n_h, 1))\n",
    "    assert (W2.shape == (n_y, n_h))\n",
    "    assert (b2.shape == (n_y, 1))\n",
    "    \n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2}\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[-0.00416758 -0.00056267]\n",
      " [-0.02136196  0.01640271]\n",
      " [-0.01793436 -0.00841747]\n",
      " [ 0.00502881 -0.01245288]]\n",
      "b1 = [[0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]]\n",
      "W2 = [[-0.01057952 -0.00909008  0.00551454  0.02292208]]\n",
      "b2 = [[0.]]\n"
     ]
    }
   ],
   "source": [
    "n_x, n_h, n_y = initialize_parameters_test_case()\n",
    "\n",
    "parameters = initialize_parameters(n_x, n_h, n_y)\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:90%\">\n",
    "  <tr>\n",
    "    <td>**W1**</td>\n",
    "    <td> [[-0.00416758 -0.00056267]\n",
    " [-0.02136196  0.01640271]\n",
    " [-0.01793436 -0.00841747]\n",
    " [ 0.00502881 -0.01245288]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**b1**</td>\n",
    "    <td> [[ 0.]\n",
    " [ 0.]\n",
    " [ 0.]\n",
    " [ 0.]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**W2**</td>\n",
    "    <td> [[-0.01057952 -0.00909008  0.00551454  0.02292208]]</td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**b2**</td>\n",
    "    <td> [[ 0.]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 - The Loop ####\n",
    "\n",
    "**Question**: Implement `forward_propagation()`.\n",
    "\n",
    "**Instructions**:\n",
    "- Look above at the mathematical representation of your classifier.\n",
    "- You can use the function `sigmoid()`. It is built-in (imported) in the notebook.\n",
    "- You can use the function `np.tanh()`. It is part of the numpy library.\n",
    "- The steps you have to implement are:\n",
    "    1. Retrieve each parameter from the dictionary \"parameters\" (which is the output of `initialize_parameters()`) by using `parameters[\"..\"]`.\n",
    "    2. Implement Forward Propagation. Compute $Z^{[1]}, A^{[1]}, Z^{[2]}$ and $A^{[2]}$ (the vector of all your predictions on all the examples in the training set).\n",
    "- Values needed in the backpropagation are stored in \"`cache`\". The `cache` will be given as an input to the backpropagation function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: forward_propagation\n",
    "\n",
    "def forward_propagation(X, parameters):\n",
    "    \"\"\"\n",
    "    Argument:\n",
    "    X -- input data of size (n_x, m)\n",
    "    parameters -- python dictionary containing your parameters (output of initialization function)\n",
    "    \n",
    "    Returns:\n",
    "    A2 -- The sigmoid output of the second activation\n",
    "    cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\"\n",
    "    \"\"\"\n",
    "    # Retrieve each parameter from the dictionary \"parameters\"\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Implement Forward Propagation to calculate A2 (probabilities)\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    Z1 = np.dot(W1,X)+b1\n",
    "    A1 = np.tanh(Z1)\n",
    "    Z2 = np.dot(W2,A1)+b2\n",
    "    A2 = sigmoid(Z2)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    assert(A2.shape == (1, X.shape[1]))\n",
    "    \n",
    "    cache = {\"Z1\": Z1,\n",
    "             \"A1\": A1,\n",
    "             \"Z2\": Z2,\n",
    "             \"A2\": A2}\n",
    "    \n",
    "    return A2, cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.26281864019752443 0.09199904522700109 -1.3076660128732143 0.21287768171914198\n"
     ]
    }
   ],
   "source": [
    "X_assess, parameters = forward_propagation_test_case()\n",
    "A2, cache = forward_propagation(X_assess, parameters)\n",
    "\n",
    "# Note: we use the mean here just to make sure that your output matches ours. \n",
    "print(np.mean(cache['Z1']) ,np.mean(cache['A1']),np.mean(cache['Z2']),np.mean(cache['A2']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table style=\"width:50%\">\n",
    "  <tr>\n",
    "    <td> 0.262818640198 0.091999045227 -1.30766601287 0.212877681719 </td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you have computed $A^{[2]}$ (in the Python variable \"`A2`\"), which contains $a^{[2](i)}$ for every example, you can compute the cost function as follows:\n",
    "\n",
    "$$J = - \\frac{1}{m} \\sum\\limits_{i = 1}^{m} \\large{(} \\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right) \\large{)} \\small\\tag{13}$$\n",
    "\n",
    "**Exercise**: Implement `compute_cost()` to compute the value of the cost $J$.\n",
    "\n",
    "**Instructions**:\n",
    "- There are many ways to implement the cross-entropy loss. To help you, we give you how we would have implemented\n",
    "$- \\sum\\limits_{i=0}^{m}  y^{(i)}\\log(a^{[2](i)})$:\n",
    "```python\n",
    "logprobs = np.multiply(np.log(A2),Y)\n",
    "cost = - np.sum(logprobs)                # no need to use a for loop!\n",
    "```\n",
    "\n",
    "(you can use either `np.multiply()` and then `np.sum()` or directly `np.dot()`).  \n",
    "Note that if you use `np.multiply` followed by `np.sum` the end result will be a type `float`, whereas if you use `np.dot`, the result will be a 2D numpy array.  We can use `np.squeeze()` to remove redundant dimensions (in the case of single float, this will be reduced to a zero-dimension array). We can cast the array as a type `float` using `float()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: compute_cost\n",
    "\n",
    "def compute_cost(A2, Y, parameters):\n",
    "    \"\"\"\n",
    "    Computes the cross-entropy cost given in equation (13)\n",
    "    \n",
    "    Arguments:\n",
    "    A2 -- The sigmoid output of the second activation, of shape (1, number of examples)\n",
    "    Y -- \"true\" labels vector of shape (1, number of examples)\n",
    "    parameters -- python dictionary containing your parameters W1, b1, W2 and b2\n",
    "    [Note that the parameters argument is not used in this function, \n",
    "    but the auto-grader currently expects this parameter.\n",
    "    Future version of this notebook will fix both the notebook \n",
    "    and the auto-grader so that `parameters` is not needed.\n",
    "    For now, please include `parameters` in the function signature,\n",
    "    and also when invoking this function.]\n",
    "    \n",
    "    Returns:\n",
    "    cost -- cross-entropy cost given equation (13)\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    m = Y.shape[1] # number of example\n",
    "\n",
    "    # Compute the cross-entropy cost\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    logprobs = np.multiply(np.log(A2),Y)\n",
    "    cost = - np.sum(logprobs)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    cost = float(np.squeeze(cost))  # makes sure cost is the dimension we expect. \n",
    "                                    # E.g., turns [[17]] into 17 \n",
    "    assert(isinstance(cost, float))\n",
    "    \n",
    "    return cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cost = 0.6926858869721941\n"
     ]
    }
   ],
   "source": [
    "A2, Y_assess, parameters = compute_cost_test_case()\n",
    "\n",
    "print(\"cost = \" + str(compute_cost(A2, Y_assess, parameters)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table style=\"width:20%\">\n",
    "  <tr>\n",
    "    <td>**cost**</td>\n",
    "    <td> 0.693058761... </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the cache computed during forward propagation, you can now implement backward propagation.\n",
    "\n",
    "**Question**: Implement the function `backward_propagation()`.\n",
    "\n",
    "**Instructions**:\n",
    "Backpropagation is usually the hardest (most mathematical) part in deep learning. To help you, here again is the slide from the lecture on backpropagation. You'll want to use the six equations on the right of this slide, since you are building a vectorized implementation.  \n",
    "\n",
    "<img src=\"images/grad_summary.png\" style=\"width:600px;height:300px;\">\n",
    "\n",
    "<!--\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)} } = \\frac{1}{m} (a^{[2](i)} - y^{(i)})$\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial W_2 } = \\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)} } a^{[1] (i) T} $\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial b_2 } = \\sum_i{\\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)}}}$\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial z_{1}^{(i)} } =  W_2^T \\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)} } * ( 1 - a^{[1] (i) 2}) $\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial W_1 } = \\frac{\\partial \\mathcal{J} }{ \\partial z_{1}^{(i)} }  X^T $\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} _i }{ \\partial b_1 } = \\sum_i{\\frac{\\partial \\mathcal{J} }{ \\partial z_{1}^{(i)}}}$\n",
    "\n",
    "- Note that $*$ denotes elementwise multiplication.\n",
    "- The notation you will use is common in deep learning coding:\n",
    "    - dW1 = $\\frac{\\partial \\mathcal{J} }{ \\partial W_1 }$\n",
    "    - db1 = $\\frac{\\partial \\mathcal{J} }{ \\partial b_1 }$\n",
    "    - dW2 = $\\frac{\\partial \\mathcal{J} }{ \\partial W_2 }$\n",
    "    - db2 = $\\frac{\\partial \\mathcal{J} }{ \\partial b_2 }$\n",
    "    \n",
    "!-->\n",
    "\n",
    "- Tips:\n",
    "    - To compute dZ1 you'll need to compute $g^{[1]'}(Z^{[1]})$. Since $g^{[1]}(.)$ is the tanh activation function, if $a = g^{[1]}(z)$ then $g^{[1]'}(z) = 1-a^2$. So you can compute \n",
    "    $g^{[1]'}(Z^{[1]})$ using `(1 - np.power(A1, 2))`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: backward_propagation\n",
    "\n",
    "def backward_propagation(parameters, cache, X, Y):\n",
    "    \"\"\"\n",
    "    Implement the backward propagation using the instructions above.\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing our parameters \n",
    "    cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\".\n",
    "    X -- input data of shape (2, number of examples)\n",
    "    Y -- \"true\" labels vector of shape (1, number of examples)\n",
    "    \n",
    "    Returns:\n",
    "    grads -- python dictionary containing your gradients with respect to different parameters\n",
    "    \"\"\"\n",
    "    m = X.shape[1]\n",
    "    \n",
    "    # First, retrieve W1 and W2 from the dictionary \"parameters\".\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    W1 = None\n",
    "    W2 = None\n",
    "    ### END CODE HERE ###\n",
    "        \n",
    "    # Retrieve also A1 and A2 from dictionary \"cache\".\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    A1 = None\n",
    "    A2 = None\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Backward propagation: calculate dW1, db1, dW2, db2. \n",
    "    ### START CODE HERE ### (≈ 6 lines of code, corresponding to 6 equations on slide above)\n",
    "    dZ2 = None\n",
    "    dW2 = None\n",
    "    db2 = None\n",
    "    dZ1 = None\n",
    "    dW1 = None\n",
    "    db1 = None\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    grads = {\"dW1\": dW1,\n",
    "             \"db1\": db1,\n",
    "             \"dW2\": dW2,\n",
    "             \"db2\": db2}\n",
    "    \n",
    "    return grads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dW1 = [[ 0.00301023 -0.00747267]\n",
      " [ 0.00257968 -0.00641288]\n",
      " [-0.00156892  0.003893  ]\n",
      " [-0.00652037  0.01618243]]\n",
      "db1 = [[ 0.00176201]\n",
      " [ 0.00150995]\n",
      " [-0.00091736]\n",
      " [-0.00381422]]\n",
      "dW2 = [[ 0.00078841  0.01765429 -0.00084166 -0.01022527]]\n",
      "db2 = [[-0.16655712]]\n"
     ]
    }
   ],
   "source": [
    "parameters, cache, X_assess, Y_assess = backward_propagation_test_case()\n",
    "\n",
    "grads = backward_propagation(parameters, cache, X_assess, Y_assess)\n",
    "print (\"dW1 = \"+ str(grads[\"dW1\"]))\n",
    "print (\"db1 = \"+ str(grads[\"db1\"]))\n",
    "print (\"dW2 = \"+ str(grads[\"dW2\"]))\n",
    "print (\"db2 = \"+ str(grads[\"db2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected output**:\n",
    "\n",
    "\n",
    "\n",
    "<table style=\"width:80%\">\n",
    "  <tr>\n",
    "    <td>**dW1**</td>\n",
    "    <td> [[ 0.00301023 -0.00747267]\n",
    " [ 0.00257968 -0.00641288]\n",
    " [-0.00156892  0.003893  ]\n",
    " [-0.00652037  0.01618243]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**db1**</td>\n",
    "    <td>  [[ 0.00176201]\n",
    " [ 0.00150995]\n",
    " [-0.00091736]\n",
    " [-0.00381422]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**dW2**</td>\n",
    "    <td> [[ 0.00078841  0.01765429 -0.00084166 -0.01022527]] </td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**db2**</td>\n",
    "    <td> [[-0.16655712]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question**: Implement the update rule. Use gradient descent. You have to use (dW1, db1, dW2, db2) in order to update (W1, b1, W2, b2).\n",
    "\n",
    "**General gradient descent rule**: $ \\theta = \\theta - \\alpha \\frac{\\partial J }{ \\partial \\theta }$ where $\\alpha$ is the learning rate and $\\theta$ represents a parameter.\n",
    "\n",
    "**Illustration**: The gradient descent algorithm with a good learning rate (converging) and a bad learning rate (diverging). Images courtesy of Adam Harley.\n",
    "\n",
    "<img src=\"images/sgd.gif\" style=\"width:400;height:400;\"> <img src=\"images/sgd_bad.gif\" style=\"width:400;height:400;\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: update_parameters\n",
    "\n",
    "def update_parameters(parameters, grads, learning_rate = 1.2):\n",
    "    \"\"\"\n",
    "    Updates parameters using the gradient descent update rule given above\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing your parameters \n",
    "    grads -- python dictionary containing your gradients \n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your updated parameters \n",
    "    \"\"\"\n",
    "    # Retrieve each parameter from the dictionary \"parameters\"\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = None\n",
    "    b1 = None\n",
    "    W2 = None\n",
    "    b2 = None\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Retrieve each gradient from the dictionary \"grads\"\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    dW1 = None\n",
    "    db1 = None\n",
    "    dW2 = None\n",
    "    db2 = None\n",
    "    ## END CODE HERE ###\n",
    "    \n",
    "    # Update rule for each parameter\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = None\n",
    "    b1 = None\n",
    "    W2 = None\n",
    "    b2 = None\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2}\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[-0.00643025  0.01936718]\n",
      " [-0.02410458  0.03978052]\n",
      " [-0.01653973 -0.02096177]\n",
      " [ 0.01046864 -0.05990141]]\n",
      "b1 = [[  1.21732533e-05]\n",
      " [  2.12263977e-05]\n",
      " [  1.36755874e-05]\n",
      " [  1.05251698e-05]]\n",
      "W2 = [[-0.01041081 -0.04463285  0.01758031  0.04747113]]\n",
      "b2 = [[ 0.00010457]]\n"
     ]
    }
   ],
   "source": [
    "parameters, grads = update_parameters_test_case()\n",
    "parameters = update_parameters(parameters, grads)\n",
    "\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "\n",
    "<table style=\"width:80%\">\n",
    "  <tr>\n",
    "    <td>**W1**</td>\n",
    "    <td> [[-0.00643025  0.01936718]\n",
    " [-0.02410458  0.03978052]\n",
    " [-0.01653973 -0.02096177]\n",
    " [ 0.01046864 -0.05990141]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**b1**</td>\n",
    "    <td> [[ -1.02420756e-06]\n",
    " [  1.27373948e-05]\n",
    " [  8.32996807e-07]\n",
    " [ -3.20136836e-06]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**W2**</td>\n",
    "    <td> [[-0.01041081 -0.04463285  0.01758031  0.04747113]] </td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**b2**</td>\n",
    "    <td> [[ 0.00010457]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 - Integrate parts 4.1, 4.2 and 4.3 in nn_model() ####\n",
    "\n",
    "**Question**: Build your neural network model in `nn_model()`.\n",
    "\n",
    "**Instructions**: The neural network model has to use the previous functions in the right order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: nn_model\n",
    "\n",
    "def nn_model(X, Y, n_h, num_iterations = 10000, print_cost=False):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    X -- dataset of shape (2, number of examples)\n",
    "    Y -- labels of shape (1, number of examples)\n",
    "    n_h -- size of the hidden layer\n",
    "    num_iterations -- Number of iterations in gradient descent loop\n",
    "    print_cost -- if True, print the cost every 1000 iterations\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- parameters learnt by the model. They can then be used to predict.\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(3)\n",
    "    n_x = layer_sizes(X, Y)[0]\n",
    "    n_y = layer_sizes(X, Y)[2]\n",
    "    \n",
    "    # Initialize parameters\n",
    "    ### START CODE HERE ### (≈ 1 line of code)\n",
    "    parameters = None\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Loop (gradient descent)\n",
    "\n",
    "    for i in range(0, num_iterations):\n",
    "         \n",
    "        ### START CODE HERE ### (≈ 4 lines of code)\n",
    "        # Forward propagation. Inputs: \"X, parameters\". Outputs: \"A2, cache\".\n",
    "        A2, cache = None\n",
    "        \n",
    "        # Cost function. Inputs: \"A2, Y, parameters\". Outputs: \"cost\".\n",
    "        cost = None\n",
    " \n",
    "        # Backpropagation. Inputs: \"parameters, cache, X, Y\". Outputs: \"grads\".\n",
    "        grads = None\n",
    " \n",
    "        # Gradient descent parameter update. Inputs: \"parameters, grads\". Outputs: \"parameters\".\n",
    "        parameters = None\n",
    "        \n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Print the cost every 1000 iterations\n",
    "        if print_cost and i % 1000 == 0:\n",
    "            print (\"Cost after iteration %i: %f\" %(i, cost))\n",
    "\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.692739\n",
      "Cost after iteration 1000: 0.000218\n",
      "Cost after iteration 2000: 0.000107\n",
      "Cost after iteration 3000: 0.000071\n",
      "Cost after iteration 4000: 0.000053\n",
      "Cost after iteration 5000: 0.000042\n",
      "Cost after iteration 6000: 0.000035\n",
      "Cost after iteration 7000: 0.000030\n",
      "Cost after iteration 8000: 0.000026\n",
      "Cost after iteration 9000: 0.000023\n",
      "W1 = [[-0.65848169  1.21866811]\n",
      " [-0.76204273  1.39377573]\n",
      " [ 0.5792005  -1.10397703]\n",
      " [ 0.76773391 -1.41477129]]\n",
      "b1 = [[ 0.287592  ]\n",
      " [ 0.3511264 ]\n",
      " [-0.2431246 ]\n",
      " [-0.35772805]]\n",
      "W2 = [[-2.45566237 -3.27042274  2.00784958  3.36773273]]\n",
      "b2 = [[ 0.20459656]]\n"
     ]
    }
   ],
   "source": [
    "X_assess, Y_assess = nn_model_test_case()\n",
    "parameters = nn_model(X_assess, Y_assess, 4, num_iterations=10000, print_cost=True)\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:90%\">\n",
    "\n",
    "<tr> \n",
    "    <td> \n",
    "        **cost after iteration 0**\n",
    "    </td>\n",
    "    <td> \n",
    "        0.692739\n",
    "    </td>\n",
    "</tr>\n",
    "\n",
    "<tr> \n",
    "    <td> \n",
    "        <center> $\\vdots$ </center>\n",
    "    </td>\n",
    "    <td> \n",
    "        <center> $\\vdots$ </center>\n",
    "    </td>\n",
    "</tr>\n",
    "\n",
    "  <tr>\n",
    "    <td>**W1**</td>\n",
    "    <td> [[-0.65848169  1.21866811]\n",
    " [-0.76204273  1.39377573]\n",
    " [ 0.5792005  -1.10397703]\n",
    " [ 0.76773391 -1.41477129]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**b1**</td>\n",
    "    <td> [[ 0.287592  ]\n",
    " [ 0.3511264 ]\n",
    " [-0.2431246 ]\n",
    " [-0.35772805]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**W2**</td>\n",
    "    <td> [[-2.45566237 -3.27042274  2.00784958  3.36773273]] </td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**b2**</td>\n",
    "    <td> [[ 0.20459656]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 Predictions\n",
    "\n",
    "**Question**: Use your model to predict by building predict().\n",
    "Use forward propagation to predict results.\n",
    "\n",
    "**Reminder**: predictions = $y_{prediction} = \\mathbb 1 \\text{{activation > 0.5}} = \\begin{cases}\n",
    "      1 & \\text{if}\\ activation > 0.5 \\\\\n",
    "      0 & \\text{otherwise}\n",
    "    \\end{cases}$  \n",
    "    \n",
    "As an example, if you would like to set the entries of a matrix X to 0 and 1 based on a threshold you would do: ```X_new = (X > threshold)```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: predict\n",
    "\n",
    "def predict(parameters, X):\n",
    "    \"\"\"\n",
    "    Using the learned parameters, predicts a class for each example in X\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing your parameters \n",
    "    X -- input data of size (n_x, m)\n",
    "    \n",
    "    Returns\n",
    "    predictions -- vector of predictions of our model (red: 0 / blue: 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    # Computes probabilities using forward propagation, and classifies to 0/1 using 0.5 as the threshold.\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    A2, cache = None\n",
    "    predictions = None\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions mean = 0.666666666667\n"
     ]
    }
   ],
   "source": [
    "parameters, X_assess = predict_test_case()\n",
    "\n",
    "predictions = predict(parameters, X_assess)\n",
    "print(\"predictions mean = \" + str(np.mean(predictions)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "\n",
    "<table style=\"width:40%\">\n",
    "  <tr>\n",
    "    <td>**predictions mean**</td>\n",
    "    <td> 0.666666666667 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is time to run the model and see how it performs on a planar dataset. Run the following code to test your model with a single hidden layer of $n_h$ hidden units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.693048\n",
      "Cost after iteration 1000: 0.288083\n",
      "Cost after iteration 2000: 0.254385\n",
      "Cost after iteration 3000: 0.233864\n",
      "Cost after iteration 4000: 0.226792\n",
      "Cost after iteration 5000: 0.222644\n",
      "Cost after iteration 6000: 0.219731\n",
      "Cost after iteration 7000: 0.217504\n",
      "Cost after iteration 8000: 0.219471\n",
      "Cost after iteration 9000: 0.218612\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f6109aee470>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEWCAYAAABmE+CbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f613dbb9b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build a model with a n_h-dimensional hidden layer\n",
    "parameters = nn_model(X, Y, n_h = 4, num_iterations = 10000, print_cost=True)\n",
    "\n",
    "# Plot the decision boundary\n",
    "plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)\n",
    "plt.title(\"Decision Boundary for hidden layer size \" + str(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:40%\">\n",
    "  <tr>\n",
    "    <td>**Cost after iteration 9000**</td>\n",
    "    <td> 0.218607 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 90%\n"
     ]
    }
   ],
   "source": [
    "# Print accuracy\n",
    "predictions = predict(parameters, X)\n",
    "print ('Accuracy: %d' % float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100) + '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:15%\">\n",
    "  <tr>\n",
    "    <td>**Accuracy**</td>\n",
    "    <td> 90% </td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy is really high compared to Logistic Regression. The model has learnt the leaf patterns of the flower! Neural networks are able to learn even highly non-linear decision boundaries, unlike logistic regression. \n",
    "\n",
    "Now, let's try out several hidden layer sizes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.6 - Tuning hidden layer size (optional/ungraded exercise) ###\n",
    "\n",
    "Run the following code. It may take 1-2 minutes. You will observe different behaviors of the model for various hidden layer sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for 1 hidden units: 67.5 %\n",
      "Accuracy for 2 hidden units: 67.25 %\n",
      "Accuracy for 3 hidden units: 90.75 %\n",
      "Accuracy for 4 hidden units: 90.5 %\n",
      "Accuracy for 5 hidden units: 91.25 %\n",
      "Accuracy for 20 hidden units: 90.0 %\n",
      "Accuracy for 50 hidden units: 90.25 %\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7IAAAWhCAYAAACoEqz7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERERERENFAayRERERERENFD+f/GIE63efLDwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6109ae3898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This may take about 2 minutes to run\n",
    "\n",
    "plt.figure(figsize=(16, 32))\n",
    "hidden_layer_sizes = [1, 2, 3, 4, 5, 20, 50]\n",
    "for i, n_h in enumerate(hidden_layer_sizes):\n",
    "    plt.subplot(5, 2, i+1)\n",
    "    plt.title('Hidden Layer of size %d' % n_h)\n",
    "    parameters = nn_model(X, Y, n_h, num_iterations = 5000)\n",
    "    plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)\n",
    "    predictions = predict(parameters, X)\n",
    "    accuracy = float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100)\n",
    "    print (\"Accuracy for {} hidden units: {} %\".format(n_h, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation**:\n",
    "- The larger models (with more hidden units) are able to fit the training set better, until eventually the largest models overfit the data. \n",
    "- The best hidden layer size seems to be around n_h = 5. Indeed, a value around here seems to  fits the data well without also incurring noticeable overfitting.\n",
    "- You will also learn later about regularization, which lets you use very large models (such as n_h = 50) without much overfitting. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Optional questions**:\n",
    "\n",
    "**Note**: Remember to submit the assignment by clicking the blue \"Submit Assignment\" button at the upper-right. \n",
    "\n",
    "Some optional/ungraded questions that you can explore if you wish: \n",
    "- What happens when you change the tanh activation for a sigmoid activation or a ReLU activation?\n",
    "- Play with the learning_rate. What happens?\n",
    "- What if we change the dataset? (See part 5 below!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**You've learnt to:**\n",
    "- Build a complete neural network with a hidden layer\n",
    "- Make a good use of a non-linear unit\n",
    "- Implemented forward propagation and backpropagation, and trained a neural network\n",
    "- See the impact of varying the hidden layer size, including overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice work! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5) Performance on other datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "If you want, you can rerun the whole notebook (minus the dataset part) for each of the following datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5QAUCoWihaIcgEKhULRQ/h9ZxToGcHfUcgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f610a052fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Datasets\n",
    "noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure = load_extra_datasets()\n",
    "\n",
    "datasets = {\"noisy_circles\": noisy_circles,\n",
    "            \"noisy_moons\": noisy_moons,\n",
    "            \"blobs\": blobs,\n",
    "            \"gaussian_quantiles\": gaussian_quantiles}\n",
    "\n",
    "### START CODE HERE ### (choose your dataset)\n",
    "dataset = \"noisy_moons\"\n",
    "### END CODE HERE ###\n",
    "\n",
    "X, Y = datasets[dataset]\n",
    "X, Y = X.T, Y.reshape(1, Y.shape[0])\n",
    "\n",
    "# make blobs binary\n",
    "if dataset == \"blobs\":\n",
    "    Y = Y%2\n",
    "\n",
    "# Visualize the data\n",
    "plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap=plt.cm.Spectral);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congrats on finishing this Programming Assignment!\n",
    "\n",
    "Reference:\n",
    "- http://scs.ryerson.ca/~aharley/neural-networks/\n",
    "- http://cs231n.github.io/neural-networks-case-study/"
   ]
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "neural-networks-deep-learning",
   "graded_item_id": "wRuwL",
   "launcher_item_id": "NI888"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
